Abstract: The present disclosure discloses systems and methods for content enhancement using QCFA sensors of an electronic device. Embodiments herein include previewing contents in a field of view (FOV) of a first QCFA sensor, wherein the first QCFA sensor is operating in a first mode. Embodiments herein include detecting whether a zoom level of the contents previewed meets a first zoom-level criteria, and whether a light condition of the contents previewed meets a light criteria. Further, the first QCFA sensor may be configured to switch from a first mode to a second mode in order to enhance a resolution of the contents being previewed in the FOV of the first QCFA sensor in response to detecting that the zoom level of the contents previewed meets the first zoom-level criteria, and that the light condition of the contents previewed meets the light criteria.

Abstract: The present disclosure discloses systems and methods for content enhancement using QCFA sensors of an electronic device. Embodiments herein include previewing contents in a field of view (FOV) of a first QCFA sensor, wherein the first QCFA sensor is operating in a first mode. Embodiments herein include detecting whether a zoom level of the contents previewed meets a first zoom-level criteria, and whether a light condition of the contents previewed meets a light criteria. Further, the first QCFA sensor may be configured to switch from a first mode to a second mode in order to enhance a resolution of the contents being previewed in the FOV of the first QCFA sensor in response to detecting that the zoom level of the contents previewed meets the first zoom-level criteria, and that the light condition of the contents previewed meets the light criteria.

Abstract: Systems, methods, and non-transitory computer readable media can determine one or more user-related metrics relating to each page of a plurality of pages associated with an administrator based on a first machine learning model. One or more recommendations relating to each page of the plurality of pages can be determined based on a second machine learning model. One or more pages of the plurality of pages for which to display cards including page updates in a feed of the administrator can be determined, based on the determined user-related metrics and the determined recommendations.

Abstract: The social networking system monitors implicit interactions between a user and objects of the social networking system with which the user has not established a connection. Based on the implicit interactions between the user and an object, the social networking system identifies a soft connection between the user and the object. The social networking system may then identify soft connections to include in a candidate list of soft connections to recommend to the user. The social networking system may also extract signals from the set of candidate list of soft connections, and may use the extracted signals to rank the soft connections in the list of candidate soft connections. The social networking system may then recommend soft connections to the user based on the rank associated with the soft connections in the candidate list of soft connections.

Abstract: Systems, methods, and non-transitory computer readable media are configured to determine a likelihood of a rejection of a notification proposed for delivery to a recipient. A delivery determination for the notification can be performed. Subsequently, the notification can be delivered to the recipient based on the delivery determination.

Abstract: Systems, methods, and non-transitory computer readable media are configured to receive values associated with features corresponding to an instance involving a page of a social networking system and an administrator of the page. The values associated with the features are applied to a machine learning model. A probability that the administrator of the page will take action on the page in response to receipt of an electronic notification provided to the administrator is determined based on the machine learning model.

Abstract: Systems, methods, and non-transitory computer readable media can determine one or more user-related metrics relating to each page of a plurality of pages associated with an administrator based on a first machine learning model. One or more recommendations relating to each page of the plurality of pages can be determined based on a second machine learning model. One or more pages of the plurality of pages for which to display cards including page updates in a feed of the administrator can be determined, based on the determined user-related metrics and the determined recommendations.

Abstract: Systems, methods, and non-transitory computer-readable media can train a machine learning model to classify at least one user account as either a first type of account or a second type of account based at least in part on one or more respective features corresponding to the user account and determine that a first user account that was created as the first type of account should be converted to the second type of account based at least in part on the machine learning model.

Abstract: The social networking system monitors implicit interactions between a user and objects of the social networking system with which the user has not established a connection. Based on the implicit interactions between the user and an object, the social networking system identifies a soft connection between the user and the object. The social networking system may then identify soft connections to include in a candidate list of soft connections to recommend to the user. The social networking system may also extract signals from the set of candidate list of soft connections, and may use the extracted signals to rank the soft connections in the list of candidate soft connections. The social networking system may then recommend soft connections to the user based on the rank associated with the soft connections in the candidate list of soft connections.

Abstract: The social networking system monitors implicit interactions between a user and objects of the social networking system with which the user has not established a connection. Based on the implicit interactions between the user and an object, the social networking system identifies a soft connection between the user and the object. The social networking system may then identify soft connections to include in a candidate list of soft connections to recommend to the user. The social networking system may also extract signals from the set of candidate list of soft connections, and may use the extracted signals to rank the soft connections in the list of candidate soft connections. The social networking system may then recommend soft connections to the user based on the rank associated with the soft connections in the candidate list of soft connections.

Abstract: The social networking system monitors implicit interactions between a user and objects of the social networking system with which the user has not established a connection. Based on the implicit interactions between the user and an object, the social networking system identifies a soft connection between the user and the object. The social networking system may then identify soft connections to include in a candidate list of soft connections to recommend to the user. The social networking system may also extract signals from the set of candidate list of soft connections, and may use the extracted signals to rank the soft connections in the list of candidate soft connections. The social networking system may then recommend soft connections to the user based on the rank associated with the soft connections in the candidate list of soft connections.